def main():
points = CIRCLE.random_uniform(n_samples=N_POINTS, bound=None)
centers, weights, clusters, n_iterations = METRIC.optimal_quantization(
points=points,
n_centers=N_CENTERS,
n_repetitions=N_REPETITIONS,
tolerance=TOLERANCE)
plt.figure(0)
visualization.plot(points=centers, space='S1', color='red')
plt.figure(1)
circle = visualization.Circle()
circle.draw()
for i in range(N_CENTERS):
circle.draw_points(points=clusters[i])
def main():
circle = Hypersphere(dimension=1)
data = circle.random_uniform(n_samples=1000)
n_clusters = 5
clustering = OnlineKMeans(metric=circle.metric, n_clusters=n_clusters)
clustering = clustering.fit(data)
plt.figure(0)
visualization.plot(points=clustering.cluster_centers_, space='S1',
color='red')
plt.show()
plt.figure(1)
ax = plt.axes()
circle_plot = visualization.Circle()
circle_plot.draw(ax=ax)
for i in range(n_clusters):
cluster = data[clustering.labels_ == i, :]
circle_plot.draw_points(ax=ax, points=cluster)
plt.show()